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<article id="content">
<header>
<h1 class="title">Module <code>pandas_profiling.visualisation.plot</code></h1>
</header>
<section id="section-intro">
<p>Plot functions for the profiling report.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">&#34;&#34;&#34;Plot functions for the profiling report.&#34;&#34;&#34;

from typing import Union

import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from pandas.plotting import register_matplotlib_converters
from pandas_profiling.visualisation.utils import plot_360_n0sc0pe
from pkg_resources import resource_filename

from pandas_profiling.config import config
from pandas_profiling.model.base import Variable

register_matplotlib_converters()
matplotlib.style.use(resource_filename(__name__, &#34;pandas_profiling.mplstyle&#34;))
sns.set_style(style=&#34;white&#34;)


def _plot_histogram(
    series: pd.Series,
    series_description: dict,
    bins: Union[int, np.ndarray],
    figsize: tuple = (6, 4),
):
    &#34;&#34;&#34;Plot an histogram from the data and return the AxesSubplot object.

    Args:
        series: The data to plot
        figsize: The size of the figure (width, height) in inches, default (6,4)
        bins: number of bins (int for equal size, ndarray for variable size)

    Returns:
        The histogram plot.


    &#34;&#34;&#34;
    if series_description[&#34;type&#34;] == Variable.TYPE_DATE:
        # Workaround for https://github.com/pandas-dev/pandas/issues/17372
        fig = plt.figure(figsize=figsize)
        plot = fig.add_subplot(111)
        plot.set_ylabel(&#34;Frequency&#34;)
        plot.hist(
            series.dropna().values,
            facecolor=config[&#34;html&#34;][&#34;style&#34;][&#34;primary_color&#34;].get(str),
            bins=bins,
        )

    else:
        plot = series.plot(
            kind=&#34;hist&#34;,
            figsize=figsize,
            facecolor=config[&#34;html&#34;][&#34;style&#34;][&#34;primary_color&#34;].get(str),
            bins=bins,
        )
    return plot


def histogram(
    series: pd.Series, series_description: dict, bins: Union[int, np.ndarray]
) -&gt; str:
    &#34;&#34;&#34;Plot an histogram of the data.

    Args:
      series_description:
      series: The data to plot.
      bins: number of bins (int for equal size, ndarray for variable size)

    Returns:
      The resulting histogram encoded as a string.

    &#34;&#34;&#34;
    plot = _plot_histogram(series, series_description, bins)
    plot.xaxis.set_tick_params(rotation=45)
    plot.figure.tight_layout()

    return plot_360_n0sc0pe(plt)


def mini_histogram(
    series: pd.Series, series_description: dict, bins: Union[int, np.ndarray]
) -&gt; str:
    &#34;&#34;&#34;Plot a small (mini) histogram of the data.

    Args:
      series_description:
      series: The data to plot.
      bins: number of bins (int for equal size, ndarray for variable size)

    Returns:
      The resulting mini histogram encoded as a string.
    &#34;&#34;&#34;
    plot = _plot_histogram(series, series_description, bins, figsize=(2, 1.5))
    plot.axes.get_yaxis().set_visible(False)
    plot.set_facecolor(&#34;w&#34;)

    xticks = plot.xaxis.get_major_ticks()
    for tick in xticks:
        tick.label1.set_fontsize(8)
    plot.xaxis.set_tick_params(rotation=45)
    plot.figure.tight_layout()

    return plot_360_n0sc0pe(plt)


def correlation_matrix(data: pd.DataFrame, vmin: int = -1) -&gt; str:
    &#34;&#34;&#34;Plot image of a matrix correlation.

    Args:
      data: The matrix correlation to plot.
      vmin: Minimum value of value range.

    Returns:
      The resulting correlation matrix encoded as a string.
    &#34;&#34;&#34;
    fig_cor, axes_cor = plt.subplots(1, 1)
    labels = data.columns
    matrix_image = axes_cor.imshow(
        data,
        vmin=vmin,
        vmax=1,
        interpolation=&#34;nearest&#34;,
        cmap=config[&#34;plot&#34;][&#34;correlation&#34;][&#34;cmap&#34;].get(str),
    )
    plt.colorbar(matrix_image)
    axes_cor.set_xticks(np.arange(0, data.shape[0], float(data.shape[0]) / len(labels)))
    axes_cor.set_yticks(np.arange(0, data.shape[1], float(data.shape[1]) / len(labels)))
    axes_cor.set_xticklabels(labels, rotation=90)
    axes_cor.set_yticklabels(labels)
    plt.subplots_adjust(bottom=0.2)

    return plot_360_n0sc0pe(plt)


def scatter_complex(series) -&gt; str:
    plt.ylabel(&#34;Imaginary&#34;)
    plt.xlabel(&#34;Real&#34;)

    if len(series) &gt; 1000:
        plt.hexbin(series.real, series.imag)
    else:
        plt.scatter(series.real, series.imag)

    return plot_360_n0sc0pe(plt)


def scatter_series(series, x_label=&#34;Width&#34;, y_label=&#34;Height&#34;) -&gt; str:
    &#34;&#34;&#34;

    Examples:
        &gt;&gt;&gt; scatter_series(file_sizes, &#34;Width&#34;, &#34;Height&#34;)

    Args:
        series:
        x_label:
        y_label:

    Returns:

    &#34;&#34;&#34;
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    if len(series) &gt; 1000:
        plt.hexbin(*zip(*series.tolist()))
    else:
        plt.scatter(*zip(*series.tolist()))
    return plot_360_n0sc0pe(plt)


def scatter_pairwise(series1, series2, x_label, y_label) -&gt; str:
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    if len(series1) &gt; 1000:
        color = config[&#34;html&#34;][&#34;style&#34;][&#34;primary_color&#34;].get(str)
        cmap = sns.light_palette(color, as_cmap=True)
        plt.hexbin(series1.tolist(), series2.tolist(), gridsize=15, cmap=cmap)
    else:
        plt.scatter(series1.tolist(), series2.tolist())
    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="pandas_profiling.visualisation.plot.correlation_matrix"><code class="name flex">
<span>def <span class="ident">correlation_matrix</span></span>(<span>data, vmin=-1)</span>
</code></dt>
<dd>
<section class="desc"><p>Plot image of a matrix correlation.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>data</code></strong></dt>
<dd>The matrix correlation to plot.</dd>
<dt><strong><code>vmin</code></strong></dt>
<dd>Minimum value of value range.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The resulting correlation matrix encoded as a string.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def correlation_matrix(data: pd.DataFrame, vmin: int = -1) -&gt; str:
    &#34;&#34;&#34;Plot image of a matrix correlation.

    Args:
      data: The matrix correlation to plot.
      vmin: Minimum value of value range.

    Returns:
      The resulting correlation matrix encoded as a string.
    &#34;&#34;&#34;
    fig_cor, axes_cor = plt.subplots(1, 1)
    labels = data.columns
    matrix_image = axes_cor.imshow(
        data,
        vmin=vmin,
        vmax=1,
        interpolation=&#34;nearest&#34;,
        cmap=config[&#34;plot&#34;][&#34;correlation&#34;][&#34;cmap&#34;].get(str),
    )
    plt.colorbar(matrix_image)
    axes_cor.set_xticks(np.arange(0, data.shape[0], float(data.shape[0]) / len(labels)))
    axes_cor.set_yticks(np.arange(0, data.shape[1], float(data.shape[1]) / len(labels)))
    axes_cor.set_xticklabels(labels, rotation=90)
    axes_cor.set_yticklabels(labels)
    plt.subplots_adjust(bottom=0.2)

    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
<dt id="pandas_profiling.visualisation.plot.histogram"><code class="name flex">
<span>def <span class="ident">histogram</span></span>(<span>series, series_description, bins)</span>
</code></dt>
<dd>
<section class="desc"><p>Plot an histogram of the data.</p>
<h2 id="args">Args</h2>
<dl>
<dt>series_description:</dt>
<dt><strong><code>series</code></strong></dt>
<dd>The data to plot.</dd>
<dt><strong><code>bins</code></strong></dt>
<dd>number of bins (int for equal size, ndarray for variable size)</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The resulting histogram encoded as a string.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def histogram(
    series: pd.Series, series_description: dict, bins: Union[int, np.ndarray]
) -&gt; str:
    &#34;&#34;&#34;Plot an histogram of the data.

    Args:
      series_description:
      series: The data to plot.
      bins: number of bins (int for equal size, ndarray for variable size)

    Returns:
      The resulting histogram encoded as a string.

    &#34;&#34;&#34;
    plot = _plot_histogram(series, series_description, bins)
    plot.xaxis.set_tick_params(rotation=45)
    plot.figure.tight_layout()

    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
<dt id="pandas_profiling.visualisation.plot.mini_histogram"><code class="name flex">
<span>def <span class="ident">mini_histogram</span></span>(<span>series, series_description, bins)</span>
</code></dt>
<dd>
<section class="desc"><p>Plot a small (mini) histogram of the data.</p>
<h2 id="args">Args</h2>
<dl>
<dt>series_description:</dt>
<dt><strong><code>series</code></strong></dt>
<dd>The data to plot.</dd>
<dt><strong><code>bins</code></strong></dt>
<dd>number of bins (int for equal size, ndarray for variable size)</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The resulting mini histogram encoded as a string.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mini_histogram(
    series: pd.Series, series_description: dict, bins: Union[int, np.ndarray]
) -&gt; str:
    &#34;&#34;&#34;Plot a small (mini) histogram of the data.

    Args:
      series_description:
      series: The data to plot.
      bins: number of bins (int for equal size, ndarray for variable size)

    Returns:
      The resulting mini histogram encoded as a string.
    &#34;&#34;&#34;
    plot = _plot_histogram(series, series_description, bins, figsize=(2, 1.5))
    plot.axes.get_yaxis().set_visible(False)
    plot.set_facecolor(&#34;w&#34;)

    xticks = plot.xaxis.get_major_ticks()
    for tick in xticks:
        tick.label1.set_fontsize(8)
    plot.xaxis.set_tick_params(rotation=45)
    plot.figure.tight_layout()

    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
<dt id="pandas_profiling.visualisation.plot.scatter_complex"><code class="name flex">
<span>def <span class="ident">scatter_complex</span></span>(<span>series)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def scatter_complex(series) -&gt; str:
    plt.ylabel(&#34;Imaginary&#34;)
    plt.xlabel(&#34;Real&#34;)

    if len(series) &gt; 1000:
        plt.hexbin(series.real, series.imag)
    else:
        plt.scatter(series.real, series.imag)

    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
<dt id="pandas_profiling.visualisation.plot.scatter_pairwise"><code class="name flex">
<span>def <span class="ident">scatter_pairwise</span></span>(<span>series1, series2, x_label, y_label)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def scatter_pairwise(series1, series2, x_label, y_label) -&gt; str:
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    if len(series1) &gt; 1000:
        color = config[&#34;html&#34;][&#34;style&#34;][&#34;primary_color&#34;].get(str)
        cmap = sns.light_palette(color, as_cmap=True)
        plt.hexbin(series1.tolist(), series2.tolist(), gridsize=15, cmap=cmap)
    else:
        plt.scatter(series1.tolist(), series2.tolist())
    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
<dt id="pandas_profiling.visualisation.plot.scatter_series"><code class="name flex">
<span>def <span class="ident">scatter_series</span></span>(<span>series, x_label='Width', y_label='Height')</span>
</code></dt>
<dd>
<section class="desc"><h2 id="examples">Examples</h2>
<pre><code>&gt;&gt;&gt; scatter_series(file_sizes, "Width", "Height")
</code></pre>
<h2 id="args">Args</h2>
<p>series:
x_label:
y_label:
Returns:</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def scatter_series(series, x_label=&#34;Width&#34;, y_label=&#34;Height&#34;) -&gt; str:
    &#34;&#34;&#34;

    Examples:
        &gt;&gt;&gt; scatter_series(file_sizes, &#34;Width&#34;, &#34;Height&#34;)

    Args:
        series:
        x_label:
        y_label:

    Returns:

    &#34;&#34;&#34;
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    if len(series) &gt; 1000:
        plt.hexbin(*zip(*series.tolist()))
    else:
        plt.scatter(*zip(*series.tolist()))
    return plot_360_n0sc0pe(plt)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="pandas_profiling.visualisation" href="index.html">pandas_profiling.visualisation</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="two-column">
<li><code><a title="pandas_profiling.visualisation.plot.correlation_matrix" href="#pandas_profiling.visualisation.plot.correlation_matrix">correlation_matrix</a></code></li>
<li><code><a title="pandas_profiling.visualisation.plot.histogram" href="#pandas_profiling.visualisation.plot.histogram">histogram</a></code></li>
<li><code><a title="pandas_profiling.visualisation.plot.mini_histogram" href="#pandas_profiling.visualisation.plot.mini_histogram">mini_histogram</a></code></li>
<li><code><a title="pandas_profiling.visualisation.plot.scatter_complex" href="#pandas_profiling.visualisation.plot.scatter_complex">scatter_complex</a></code></li>
<li><code><a title="pandas_profiling.visualisation.plot.scatter_pairwise" href="#pandas_profiling.visualisation.plot.scatter_pairwise">scatter_pairwise</a></code></li>
<li><code><a title="pandas_profiling.visualisation.plot.scatter_series" href="#pandas_profiling.visualisation.plot.scatter_series">scatter_series</a></code></li>
</ul>
</li>
</ul>
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